The present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model. Editor D. Koutsoyiannis; Associate editor A. Montanari Citation Varouchakis, E.A., Hristopoulos, D.T., and Karatzas, G.P., 2012. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application. Hydrological Sciences Journal, 57 (7), 1404–1419.

To date only few studies have dealt with the biogeography of microbial communities at large spatial scales, despite the importance of such information to understand and simulate ecosystem functioning. Herein, we describe the biogeographic patterns of microorganisms involved in nitrogen (N)-cycling (diazotrophs, ammonia oxidizers, denitrifiers) as well as the environmental factors shaping these patterns across the Koiliaris Critical Zone Observatory, a typical Mediterranean watershed. Our findings revealed that a proportion of variance ranging from 40 to 80% of functional genes abundance could be explained by the environmental variables monitored, with pH, soil texture, total organic carbon and potential nitrification rate being identified as the most important drivers. The spatial autocorrelation of N-functional genes ranged from 0.2 to 6.2 km and prediction maps, generated by cokriging, revealed distinct patterns of functional genes. The inclusion of functional genes in statistical modeling substantially improved the proportion of variance explained by the models, a result possibly due to the strong relationships that were identified among microbial groups. Significant relationships were set between functional groups, which were further mediated by land use (natural versus agricultural lands). These relationships, in combination with the environmental variables, allow us to provide insights regarding the ecological preferences of N-functional groups and among them the recently identified clade II of nitrous oxide reducers.